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A Case Study of Constructing Decisional DNA in Finance. Content. Knowledge Engineering (KE) Finance Domain Decisional DNA Creating Decisional DNA Testing Decisional DNA Comparing with other Applications Conclusions. Knowledge Engineering.

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content
Content

Knowledge Engineering (KE)

Finance Domain

Decisional DNA

Creating Decisional DNA

Testing Decisional DNA

Comparing with other Applications

Conclusions

knowledge engineering
Knowledge Engineering
  • Knowledge Engineering (KE) is a discipline that aims to offering solutions for complex problems by the means of integrating knowledge into computer systems.
  • It involves the use and application of several computer science domains such as artificial intelligence, knowledge representation, databases, and decision support systems, among others.
knowledge engineering1
Knowledge Engineering
  • Features associated with KE systems are human intelligence capabilities.
  • In our case, experience is the main and most appropriate source of knowledge and its use leads to useful systems with improved performance.
  • Our research paper explains the process of implementing a KE technology that can collect, use and offer trustable knowledge in several domains. Our case study: Finance domain.
finance domain
Finance Domain
  • Finance has been ruling the word since the industrial revolution, and lately, have become a cumbersome issue.
  • It has great potential and extended use of intelligent techniques.
  • KE has been applied to the finance domain in several occasions, comprising applications in AI, knowledge-base systems, expert systems, decisional support systems, heuristic systems, and the list appears without an end.
finance domain1
Finance Domain
  • All this multiple applications perform decisions in a structured and formal way (i.e. formal decision events).
  • All these formal decision events are usually disregarded once the decision is made, or even worst, if the system is queried again, the decision has to be repeated.
  • What to do with the experience gained on taking such decisions?
decisional dna
Decisional DNA

In our previous work, we have proposed a domain independent single knowledge structure for capturing, storing, improving and reusing decisional experience.

what to represent

** OBSERVED STATISTICS REPORT for scenario TVANIM **

Label Mean Standard Number of Minimum Maximum

Value Deviation Observations Value Value

TIME IN SYSTEM 26.956 34.643 83 8.202 170.770

** FILE STATISTICS REPORT for scenario TVANIM **

File Label or Average Standard Maximum Current Average

Number Input Location Length Deviation Length Leng

What to represent?

Knowledge or Experience generated by applications that confront problems in diverse methods and perform formal decision events.

A formal decision event is a decision occurrence, which was executed following strict procedures that make it structured and formal.

formal decision events

U

V

If X>70 then K = good

R

If W<2 then Z = 2

Z = 0.78

If G=blue then B = high

K = average

X = 100

H = good

RtÈRlÈRtl

W = 1.5

G = blue

Y = 210

B = high

V = 8451.54

C

2X+3Y-V <= 3450

Vl

Vt

H>=Excellent

G<>blue AND Y+70X<2500

F

Max P=3X-2Y+RQ

CtÈCl

Max K=Excellent

Min C=YQ AND B=high

FtÈFlÈFtl

Formal Decision Events

Their four components are variables, functions, constraints, and rules, and constitute the knowledge structure.

slide10

Set of Experience

Set of experience comprises a series of mathematical concepts (a logical component), together with a set of rules (a ruled based component), and built upon a specific event of decision-making (a frame component).

  • Domain Independent
  • Unique
  • Adaptable
  • Dynamic

Sets of experience are grouped according to their phenotypeor decisional liaison creating chromosomes.

slide11

Variables

Variables

Variables

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Rules

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Functions

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Grouping SOE

  • Groups of SOE by area are:

Decisional Chromosomes

  • Groups of chromosomes are =

DECISIONAL DNA

AREA 1 (marketing)

AREA 2 (finances)

AREA 3 (design)

slide12

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Decisional

Chromosome

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Decisional Gene or SOEKS

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Decisional DNA

Decisional DNA

  • SOE are grouped according to their phenotype creating Decisional Chromosomes.
  • Groups of chromosomes construct the Decisional DNA.
ke in practice
KE in Practice
  • Through our project we proposed:
    • a knowledge structure able to store and maintain experiential knowledge,
    • a solution for collecting experience that can be applied to multi-domain applications, i.e. a multi-domain knowledge representation, and,
    • a way to automate decision making by using such experience.
  • We constructed a chromosome on net income which involved the implementation of the Decisional DNA and the SOEKS.
net income
Net Income
  • Financial and marketing analysis require forecasting multiple variables, and people’s income is not the exception.
  • We used a data set called “Census Income” made public at the UCI Machine Learning website. This data set was created by a DM application. It comprises a set of 14 variables, 1 class prediction variable and 14 constraints. It contains 32561 formal decision events.
net income data set
Net Income: Data set
  • Variables
  • Age
    • Workclass
    • Fnlwgt
    • Education
    • education-num
    • marital-status
    • Occupation
    • Relationship
    • Race
    • Sex
    • capital-gain
    • capital-loss
    • hours-per-week
    • native-country
    • prediction

Constraints

Age >= 0

Workclass =

{Private,

Self-emp-not-inc,

Self-emp-inc,

Federal-gov,

Local-gov,

State-gov,

Without-pay,

Never-worked}

creating decisional dna
Creating Decisional DNA
  • Such plan required different steps:
    • Download the net income data set,
    • Build a CSV file with the collected information,
    • Convert the formal decision events into SOEKS programmatically by parsing the information,
    • Feed the net income decisional chromosome with Sets of Experience, and
    • Check the net income Decisional DNA by querying the system with samples.
creating decisional dna1
Creating Decisional DNA
  • After steps 1-2 are completed, a Java parser built up each SOE (step 3).
  • Afterwards, each SOE is uploaded onto a part of the net income decisional chromosome (step 4).
  • This net income Decisional DNA comprised 32561 SOE.
  • The net income Decisional DNA is constructed and ready to be queried.
testing decisional dna
Testing Decisional DNA
  • Our tests were performed on a Windows Vista Intel Core Duo T9400 (2.53 GHz), 4 Gb RAM, 32 bit operating system, running the SUN Java virtual machine V1.6.
  • For testing purposes, we chose several random SOE and used them as query samples.
  • Several execution times were taken on two runs (T1 and T2).
parsing time
Parsing Time
  • Executed 100 times,
  • Average parsing time = 3.18 ms
    • PT1 = 3.2045 ms,
    • PT2 = 3.1539 ms.
  • Excellent time for 32561 SOEKS loaded.
  • Partial times Variables
    • DPT1 = 3.1515 ms,
    • DPT2 = 3.0862 ms.
  • Partial times Constraints
    • DPT1 = 0.0531 ms,
    • DPT2 = 0.0515 ms.
searching time
Searching Time
  • Time taken in finding an exact match.
  • Executed 650 times,
  • Average zero time = 0.23 ms
    • ZT1 = 0.2331 ms,
    • ZT2 = 0.2393 ms.
    • Excellent time for 32561 SOEKS and the amount of comparisons performed in order to find a similarity value of zero.
similarity time
Similarity Time
  • Time taken in finding the top 5 similarity values.
  • Executed 650 times,
  • Average total time = 0.475 ms
    • TT1 = 0.4722 ms,
    • TT2 = 0.4753 ms.
    • Excellent time for knowledge retrieval on 32561 SOEKS.
geothermal energy
Geothermal Energy
  • One promising renewable energy technology is geothermal energy. A geothermal system’s performance depends upon several factors, including ambient and ground temperatures, pressures, and ground matter, etc.
  • In any geothermal system, sizing represents an important part of the system design. Thus, in order to size a geothermal system, the characteristic performance of each component in the system is required.
geothermal energy1
Geothermal Energy
  • The geothermal lab simulates traditional geothermal power cycles.
  • In order to collect knowledge and experience created for this geothermal power cycle laboratory, several instruments and sensors are positioned in the system.
  • A PC based remote operator control panel operates a software, which provides a visual user interface and collects all the real time online information of the process sensors.
ke in practice1
KE in Practice
  • Through our project we proposed:
    • a knowledge structure able to store and maintain experiential knowledge,
    • a solution for collecting experience that can be applied to experimental research, and
    • a way to automate decision making by using such experience.
  • Our plan aimed for the construction of a chromosomeon geothermal energy which involved the implementation of the Decisional DNA, and within it, the use of the SOEKS.
creating decisional dna2
Creating Decisional DNA
  • Such plan required different steps:
    • Run experiments at the geothermal laboratory,
    • Gather online information from the 43 sensors,
    • Convert information to SOEKS compliant XML,
    • Feed an ontology chromosome with SOE, and
    • Check Energy Decisional DNA by querying the system with samples.
  • 1-2 At the geothermal laboratory, experiments run continuously for 20 hours and information from each sensor was collected.
creating decisional dna3
Creating Decisional DNA
  • 3-4 Having these files, a parser written in Java, built up the XML trees which are then uploaded by using a Java API into protégé.
  • Once the geothermal chromosome is constructed and ready in protégé, an additional extension for ontologies was applied: Reflexive Ontologies (RO).
  • The last part provides the ontology with a mechanism to perform queries and some logic on the queries.
creating decisional dna4
Creating Decisional DNA

New Query

Reflexive Ontology Structure

Instance Values and Answer to Query

  • A query is used to exemplify this case study:
  • public static String SIMPLE_RFLEXIVE_QUERY="CLASS variable with the PROPERTY var_name EQUALS to X1";
creating decisional dna5
Creating Decisional DNA
  • The value type query is written in a human-like readable form which means “retrieve all the variables <sensor data> of the ontology that have the variable name X1”.
  • The execution of the code offers information about the type of query executed and the successful saving of the query executed with results within the Reflexive Ontology Structure.
conclusions
Conclusions
  • We presented a case study for the implementation of Decisional DNA on the finance domain.
  • By using the Decisional DNA, any knowledge system can gain from the advantages Decisional DNA offers:
    • versatility and dynamicity of the knowledge structure,
    • storage of day-to-day explicit experience in a single structure,
    • transportability and share ability of the knowledge, and
    • predicting capabilities based on the collected experience.